Method and system for training athletes based on athletic signatures and prescription

ABSTRACT

A method for training athletes is disclosed. The method comprises: calculating an athletic signature for an athlete comprising normalized values for the concentric net vertical impulse (CON-IMP), the average eccentric rate of force development (ECC-RFD), and the average vertical concentric force (CON-VF) for the athlete; analyzing the athletic signature; and assigning at least one training block to the athlete based on the analysis of the athletic signature.

FIELD

Embodiments of the present invention relate to athletic performance. Inparticular, embodiments of the present invention relate to systems foranalyzing athletic movement

BACKGROUND

A force plate may be used to generate data relating to athleticmovement, e.g. in the form of a jump. However, the data can be quitevoluminous as a data point may be generated once every millisecond. Thismakes analysis of the data difficult.

SUMMARY

According to a first aspect of the invention, a method for trainingathletes is disclosed. The method comprises: calculating an athleticsignature for an athlete comprising normalized values for the concentricnet vertical impulse (CON-IMP), the average eccentric rate of forcedevelopment (ECC-RFD), and the average vertical concentric force(CON-VF) for the athlete; analyzing the athletic signature; andassigning at least one training block to the athlete based on theanalysis of the athletic signature.

Other aspects of the invention will be apparent from the detaileddescription below.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a logical block diagram of a system to analyze athleticmovement, in accordance with one embodiment of the invention.

FIG. 2 shows the system of FIG. 1 implemented with a force-plate, inaccordance with one embodiment of the invention.

FIGS. 3A and 3B show, respectively, a flowchart of a method forgenerating a signature for an athlete, and a flowchart of a trainingmethod for athletes, each in accordance with one embodiment of theinvention.

FIG. 4-12 show examples of signatures, in accordance with one embodimentof the invention;

FIG. 13 shows an example of guidance, in accordance with one embodimentof the invention.

FIG. 14 shows of a method for training athletes based on signatures andprescriptions, in accordance with one embodiment of the invention.

FIG. 15 shows a high-level block diagram of hardware used to implementthe system of FIG. 1, in accordance with one embodiment of theinvention.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the invention. It will be apparent, however, to oneskilled in the art that the invention can be practiced without thesespecific details. In other instances, structures and devices are shownin block or flow diagram form only in order to avoid obscuring theinvention.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the invention. The appearance of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, various features aredescribed which may be exhibited by some embodiments and not by others.Similarly, various requirements are described which may be requirementsfor some embodiments but not other embodiments.

Moreover, although the following description contains many specifics forthe purposes of illustration, anyone skilled in the art will appreciatethat many variations and/or alterations to the details are within thescope of the present invention. Similarly, although many of the featuresof the present invention are described in terms of each other, or inconjunction with each other, one skilled in the art will appreciate thatmany of these features can be provided independently of other features.Accordingly, this description of the invention is set forth without anyloss of generality to, and without imposing limitations upon, theinvention.

Referring to FIG. 1, embodiments of the present invention disclose asystem 100 for analyzing athletic movement. For illustrative purposes,consider the athletic movement to be a vertical jump. However, it is tobe understood that the system may be used to analyze other forms ofathletic movement, such as golf and baseball swings, baseball andfootball throws, sprinting, agility, basketball shooting, and kicking.

The system 100 may, at least logically, be divided into a sensingsub-system 102, an analytical sub-system 104, and an output sub-system106.

The sensing sub-system 102 may include sensors for sensing atime-dependent variable that changes during the athletic movement. Inone embodiment, the sensing sub-system 102 may include a sensor in theform of a force-plate 108, as shown in FIG. 2. In other embodiments, thesensing sub-system 102 may include other types of sensors. For example,in one embodiment, the sensing sub-system 102 may include anaccelerometer, which may be integrated, for example, into a bracelet ora shoe pod. In use, an athlete 110 initiates a vertical jump (athleticmovement) on the force-plate 108. The force plate 108 records changes inforce over time (typically one force reading is captured eachmillisecond). An analog-to-digital converter (not shown) converts theanalog force signal into a digital signal for analysis by the analyticalsub-system 104.

The analytical sub-system 104 may include instructions to process thedigital signal in order to compile an athletic signature for the athlete110. In one embodiment, the analytical sub-system 104 extracts selectedportions of a force-time curve output by the sensing sub-system 102.Said selected portions may comprise phases of the jump including a loadphase, an explode phase, and a drive phase, as detailed below:

(a) load phase: comprises data relating to the average eccentric rate offorce development during the jump.

(b) explode phase: comprises data relating to the average relativeconcentric peak force generated during the jump, computed as averageconcentric peak force divided by the athlete's weight.

(c) drive phase: comprises data relating to the concentric relativeimpulse for the jump.

Typically, the system 100 is configured to process a plurality of jumpsfor each athlete and to store data for each athlete in the form of anathletic signature

Each athletic signature may by used to profile an athlete in terms of atleast suitability for a given sport, proneness to injury, suitabilityfor particular athletic gear (e.g. shoes), etc.

The output sub-system 106 facilitates output of athletic signatures viaprintout, display, etc. FIG. 3A shows a flowchart corresponding to amethod for generating a signature for an athlete, in accordance with oneembodiment. The method includes the following processing blocks.

Block 300: in this block force-time data for a population of athletes isstored in memory. Said force-time data may be generated by a sensingsub-system 102 in respect of each of said plurality of athletes inresponse to the said athlete performing an athletic movement, andcomprises values for a concentric net vertical impulse (CON-IMP), anaverage eccentric rate of force development (ECC-RFD), and an averagevertical concentric force (CON-VF);

Block 302: in this block a normalization of the force-time data for eachathlete based on values of the force-time data within the population ofathletes is performed;

Block 304: in this block a profile comprising an athletic signature foreach athlete in the population is generated, wherein said profilecomprises the normalized values for the concentric net vertical impulse(CON-IMP), the average eccentric rate of force development (ECC-RFD),and the average vertical concentric force (CON-VF) for the athlete.

In one embodiment, performing the normalization comprises calculating aT-score for concentric net vertical impulse (CON-IMP), average eccentricrate of force development (ECC-RFD), and average vertical concentricforce (CON-VF) for each athlete. Each T-score may be calculated as anaverage over a standard deviation.

In one embodiment, the population of athletes may comprise athletes whoplay a particular sport.

In one embodiment, the population of athletes may comprise athletes whoplay a particular position within a particular sport.

The method may further comprise analyzing the athletic signatures ofelite athletes and characterizing said signatures into an archetypalsignature corresponding to one of a role within a sport and a sport.

In one embodiment, the force-time data comprises repeating datacollected for each athlete when performing the same athletic movement atdifferent times.

FIG. 3B shows a flowchart of a training method for training athletes, inaccordance with one embodiment of the invention. The training methodincludes the following processing blocks:

Block 310: in this block a classification for signatures generated basedon movement data associated with athletes is maintained;

Block 312: in this block guidance is associated with each signature inthe classification;

Block 314: in this block a signature is assigned from the database to atleast some athletes in the database.

In one embodiment, the classification comprises a linear signature tocharacterize athletes who excel at movement in a straight line. FIG. 4shows a depiction of the linear signature in the form of a bar chart 400to show the values for the variables load, explode, and drive, inaccordance with one embodiment. For the linear signature, the load valueis less than the others by 5, in one embodiment.

In one embodiment, the classification comprises a rotational signatureto characterize athletes who excel at movement that includes an elementof rotation. FIG. 5 shows the depiction of the rotational signature inthe form of a bar chart 500, in accordance with one embodiment. For therotational signature, the explode values is less than the others by 5,in one embodiment.

In one embodiment, the classification comprises a lateral signature tocharacterize athletes who excel at lateral movements. FIG. 6 shows adepiction of the lateral signature in the form of a bar chart 600, inaccordance with one embodiment. For the lateral signature, the drivevalue is less than the others by 5, in one embodiment.

In one embodiment, the classification may include extreme signatures.These are signatures where one of the variables load, explode, drive arehigher than the other two by a threshold amount.

FIG. 7 shows a bar chart 700 corresponding to a signature classificationcalled extreme load for which the load value exceeds the other values by10.

FIG. 8 shows a bar chart 800 corresponding to a signature classificationcalled extreme explode for which the explode value exceeds the othervalues by 10.

FIG. 9 shows a bar chart 900 corresponding to a signature classificationcalled extreme drive for which the drive value exceeds the other valuesby 10.

In one embodiment, the classification may include weak signatures. Theseare signatures where one of the variables load, explode, drive is lowerthan the other two by a threshold amount.

FIG. 10 shows a bar chart 1000 corresponding to a signatureclassification called weak drive for which the drive value is less thanthe other values by 10.

FIG. 11 shows a bar chart 1100 corresponding to a signatureclassification called weak explode for which the explode value is lessthan the other values by 10.

FIG. 12 shows a bar chart 1200 corresponding to a signatureclassification called weak load for which the load value is less thanthe other values by 10.

FIG. 13 shows a matrix 1300 indicative of the type of guidance thatmight be associated with the signatures in the classification, inaccordance with one embodiment. As will be seen the matrix 1300associates particular signatures with genetic/ethnic background, sport,position in sport, injury risk, and exercise needs.

In one embodiment, by determining signatures for athletes that are goodat certain sports or certain positions within sports it is possible todetermine certain archetypical signatures associated with performanceexcellence. Column E in the matrix 1300 indicates the archetypicalsignatures for certain sports, and sport positions.

In one embodiment, the guidance may comprise at least one exerciseprotocol for at least one of transforming an athlete's signature to adesired signature and preventing injury to the athlete. The exerciseprotocol may comprise an exercise definition, a number of repetitionsassociated with the exercise, a number of sets associated with theexercise, and a schedule for performing the exercise.

The output sub-system 106 facilitates output of athletic signatures viaprintout, display, etc.

In one embodiment, the athletic signatures may be used to train athletesbased on a prescription. Elements of a prescription may include:

a) a movement, e.g. a squat;

b) an exercise, e.g., 18″ box squat;

c) a method for performing an exercise. For example, in on embodiment,the method for performing the exercise may specify a loading scheme forthe exercise which includes the number of repetitions to be performed,the speed at which each repetition is to be performed, and a weightassociated with each exercise;d) one or more performance targets for an athlete. Each performancetarget may define an achievement/milestone of an athlete that signifiesan aspect of athletic proficiency or competence.

FIG. 14 shows the steps for a training method based on signatures andprescriptions. Referring to FIG. 14 at block 1400, a signature for anathlete is calculated in accordance with the techniques alreadydisclosed herein. Each signature includes three variables. These are thenormalized values for the concentric net vertical impulse (CON-IMP), theaverage eccentric rate of force development (ECC-RFD), and the averagevertical concentric force (CON-VF) for the athlete.

At block 1402, the signature is analyzed. In one embodiment, thisanalysis may include a calculation of the relative differences in thethree variables in the signature. Statistical techniques may be used toanalyze the relative differences in the three variables in an athlete'ssignature for a whole population of athletes. Thus, significantdifferences in the three variables may be identified and associated withparticular prescriptions. Each prescription may be designed to improve aparticular aspect of an athlete's performance.

At block 1404, at least one training block may be assigned to theathlete based on the analysis of the athletic signature. In the case ofmore than one block, the blocks may be prioritized. Each block maycomprise more than one prescription.

For example, the analysis step may reveal that for a particular athlete,the value for load is 8 less than the values for explode and drive. Thiscondition may requires 2 “doses” of a exercise in a prescription,whereas if the value for load was only 4 less than the values forexplode and drive, then the prescription may only specify a dosage ofonly 1 for the exercise

Embodiments of the invention also monitor the “consumption” of aprescription. For example, an athlete may use an app on a smartphone toinput data about the prescriptions taken by the athlete. This data iscollected and analyzed to determine an efficacy of a prescription and/ora dosage, and may be used to calculate changes in a prescription ordosage. In one embodiment, the data input allows calculation of exercisechanges over time, which are then correlated with movement signaturechanges and sport statistical changes (e.g., yards rushing, receptions,etc.).

FIG. 15 shows an example of hardware 1500 that may be used to implementportions of the system 100, in accordance with one embodiment. Thehardware 1500 may include at least one processor 1502 coupled to amemory 1504. The processor 1502 may represent one or more processors(e.g., microprocessors), and the memory 1504 may represent random accessmemory (RAM) devices comprising a main storage of the hardware, as wellas any supplemental levels of memory, e.g., cache memories, non-volatileor back-up memories (e.g., programmable or flash memories), read-onlymemories, etc. In addition, the memory 1504 may be considered to includememory storage physically located elsewhere in the hardware, e.g., anycache memory in the processor 1502, as well as any storage capacity usedas a virtual memory, e.g., as stored on a mass storage device.

The hardware also typically receives a number of inputs and outputs forcommunicating information externally. For interface with a user oroperator, the hardware may include one or more user input/output devices1506 (e.g., force-plate, keyboard, mouse, etc.) and a display 1508. Foradditional storage, the hardware 1500 may also include one or more massstorage devices 1510, e.g., a Universal Serial Bus (USB) or otherremovable disk drive, a hard disk drive, a Direct Access Storage Device(DASD), an optical drive (e.g., a Compact Disk (CD) drive, a DigitalVersatile Disk (DVD) drive, etc.) and/or a USB drive, among others.Furthermore, the hardware may include an interface with one or morenetworks 1512 (e.g., a local area network (LAN), a wide area network(WAN), a wireless network, and/or the Internet among others) to permitthe communication of information with other computers coupled to thenetwork. It should be appreciated that the hardware typically includessuitable analog and/or digital interfaces between the processor 1502 andeach of the components, as is well known in the art.

The hardware 1500 operates under the control of an operating system1514, and executes application software 1516 which includes variouscomputer software applications, components, programs, objects, modules,etc. to perform the techniques described above.

In general, the routines executed to implement the embodiments of theinvention, may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and that, when readand executed by one or more processors in a computer, cause the computerto perform operations necessary to execute elements involving thevarious aspects of the invention. Moreover, while the invention has beendescribed in the context of fully functioning computers and computersystems, those skilled in the art will appreciate that the variousembodiments of the invention are capable of being distributed as aprogram product in a variety of forms, and that the invention appliesequally regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.Examples of computer-readable media include but are not limited torecordable type media such as volatile and non-volatile memory devices,USB and other removable media, hard disk drives, optical disks (e.g.,Compact Disk Read-Only Memory (CD ROMS), Digital Versatile Disks,(DVDs), etc.), flash drives among others.

Although the present invention has been described with reference tospecific exemplary embodiments, it will be evident that the variousmodification and changes can be made to these embodiments withoutdeparting from the broader spirit of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative senserather than in a restrictive sense.

The invention claimed is:
 1. A computer-based method for prescribing anexercise protocol to rehabilitate or prevent injury in athletes,comprising: generating by a sensing sub-system, analog force-time datain response to an athletic movement of an athlete; converting the analogforce-time data into a digital signal using an analog-to-digitalconverter; analyzing the digital signal in an analytical sub-system,wherein said analyzing comprises: extracting a plurality of portionsfrom the digital signal, the extracted portions comprising a pluralityof values including a concentric net vertical impulse (CON-IMP), anaverage eccentric rate of force development (ECC-RFD), and an averagevertical concentric force (CON-VF) for the athlete; generating anathletic signature by normalizing the plurality of values, wherein saidnormalizing includes calculating a T-score for each of the plurality ofvalues based on a plurality of athletic signatures aggregated in adatabase; calculating one or more relative differences between theplurality of normalized values; and comparing the one or more relativedifferences with one or more predetermined thresholds to determine asignature classification; storing the athletic signature and the one ormore relative differences in the database; determining, by one or moreprocessors, a personalized training guidance for the athlete based onthe athletic signature and the signature classification, wherein saiddetermining comprises referencing the database, wherein the databasefurther includes training guidance associated with the plurality ofathletic signatures, and wherein said personalized training guidancecomprises at least one of an exercise definition, a number ofrepetitions associated with the exercise, a number of sets associatedwith the exercise, and a schedule for performing the exercise; anddisplaying, through a computer program, the personalized trainingguidance.
 2. The method of claim 1 further comprises determining, by oneor more processors, at least one prescription.
 3. The method of claim 2,wherein each prescription comprises at least a movement and an exerciseassociated with the movement.
 4. The method of claim 3, wherein eachprescription comprises a loading scheme for performing the exercise. 5.The method of claim 4, wherein the prescription comprises an achievementtarget.
 6. A non-transitory computer-readable medium comprisinginstructions which when executed by a processing system causes saidsystem to perform a method for prescribing an exercise protocol torehabilitate or prevent injury in athletes, comprising: generating by asensing sub-system, analog force-time data in response to an athleticmovement of an athlete; converting the analog force-time data into adigital signal using an analog-to-digital converter; analyzing thedigital signal in an analytical sub-system, wherein said analyzingcomprises: extracting a plurality of portions from the digital signal,the extracted portions comprising a plurality of values including aconcentric net vertical impulse (CON-IMP), an average eccentric rate offorce development (ECC-RFD), and an average vertical concentric force(CON-VF) for the athlete; generating an athletic signature bynormalizing the plurality of values, wherein said normalizing includescalculating a T-score for each of the plurality of values based on aplurality of athletic signatures aggregated in a database; calculatingone or more relative differences between the plurality of normalizedvalues; and comparing the one or more relative differences with one ormore predetermined thresholds to determine a signature classification;storing the athletic signature and the one or more relative differencesin the database; determining, by one or more processors, a personalizedtraining guidance for the athlete based on the athletic signature andthe signature classification, wherein said determining comprisesreferencing the database, wherein the database further includes trainingguidance associated with the plurality of athletic signatures, andwherein said personalized training guidance comprises at least one of anexercise definition, a number of repetitions associated with theexercise, a number of sets associated with the exercise, and a schedulefor performing the exercise; and displaying, through a computer program,the personalized training guidance.
 7. The computer-readable medium ofclaim 6 further comprises determining, by one or more processors, atleast one prescription.
 8. The computer-readable medium of claim 7,wherein each prescription comprises at least a movement and an exerciseassociated with the movement.
 9. The computer-readable medium of claim8, wherein each prescription comprises a loading scheme for performingthe exercise.
 10. The computer-readable medium of claim 9, wherein theprescription comprises an achievement target.
 11. A system forprescribing an exercise protocol to rehabilitate or prevent injury inathletes, comprising: a processor; and a memory coupled to theprocessor, the memory storing instructions which when executed by theprocessor, causes the system to: generate by a sensing sub-system,analog force-time data in response to an athletic movement of anathlete; convert the analog force-time data into a digital signal usingan analog-to-digital converter; analyze the digital signal in ananalytical sub-system, wherein said analyzing comprises: extracting aplurality of portions from the digital signal, the extracted portionscomprising a plurality of values including a concentric net verticalimpulse (CON-IMP), an average eccentric rate of force development(ECC-RFD), and an average vertical concentric force (CON-VF) for theathlete; generating an athletic signature by normalizing the pluralityof values, wherein said normalizing includes calculating a T-score foreach of the plurality of values based on a plurality of athleticsignatures aggregated in a database; calculating one or more relativedifferences between the plurality of normalized values; and comparingthe one or more relative differences with one or more predeterminedthresholds to determine a signature classification; store the athleticsignature and the one or more relative differences in the database;determine a personalized training guidance for the athlete based on theathletic signature and the signature classification, wherein saiddetermining comprises referencing the database, wherein the databasefurther includes training guidance associated with the plurality ofathletic signatures, and wherein said personalized training guidancecomprises at least one of an exercise definition, a number ofrepetitions associated with the exercise, a number of sets associatedwith the exercise, and a schedule for performing the exercise; anddisplay, through a computer program, the personalized training guidance.12. The system of claim 11, wherein the instructions stored in memory,when executed by the processor, further cause the system to determine atleast one prescription.
 13. The system of claim 12, wherein eachprescription comprises at least a movement and an exercise associatedwith the movement.
 14. The system of claim 13, wherein each prescriptioncomprises a loading scheme for performing the exercise.
 15. The systemof claim 14, wherein the prescription comprises an achievement target.16. The system of claim 11, wherein the instructions stored in memory,when executed by the processor, further cause the system to: analyze theathletic signatures of elite athletes and characterize the athleticsignatures into an archetypal signature corresponding to one of a rolewithin a sport and a sport.